Staff Research Engineer – Post-training & Evaluation

• Define the 'Reddit Benchmark' evaluation standard: Own the methodology — not just the harness — for rigorously measuring model quality across Safety, Reasoning, representation/retrieval, and Reddit-specific knowledge.

Decide what 'Reddit-native' means in measurable terms and set the bar the org trains against.

• Own evaluation reliability and statistical rigor: Establish the science behind trustworthy evals — judge variance, multi-sample scoring, inter-rater/inter-sample agreement, sampling and temperature effects, and calibration of automated judges.

You are accountable for whether a benchmark delta is real or noise.

Drive the practice of evaluation as a release gate — offline against frozen datasets, and pre-merge in CI/CD — so regressions are caught before endpoints ship.

• Design model-as-a-judge methodology: Own judge selection, prompt design, calibration, and reliability for automated evaluation using frontier external models, enabling rapid, trustworthy iteration cycles.
• Set post-training recipes and strategy: Design SFT recipes (data mixtures, curriculum, ablation strategy) that convert base models into helpful, well-aligned endpoints; partner with engineering to scale them.
• Evaluate base and CPT checkpoints, not just endpoints: Design checkpoint-selection methodology across CPT experiments and LR studies, so we pick the right base before committing post-training compute.
• Drive synthetic data generation strategy: Define and curate high-quality instruction and evaluation sets to improve generalization where human data is scarce.
• Partner with
• Safety

Engineering: Translate high-level safety policy into concrete classification metrics, probe sets, and CI/CD unit tests — including precision/recall at threshold, label-noise handling, and false-positive taxonomy for abuse detection (HHV).

• Diagnose post-training instability: Dive into loss curves and eval logs to identify alignment tax and capability degradation, and recommend the fix.
• Lead research direction: Set technical direction for evaluation and post-training across the team, mentor engineers and scientists, and represent the work internally (and externally where appropriate).

Requirements

• 6+ years of professional ML experience (or Ph D + 4+) with a direct focus on LLM post-training and evaluation.
• Ph D or MS in CS, ML, NLP, IR, or a related quantitative field — or equivalent industry research experience.
• Deep expertise in evaluation reliability: judge/sample variance, multi-sample scoring, calibration, statistical significance, and the failure modes of automated evaluation.
• Strong experience building custom, domain-specific evaluation harnesses (e. g., lm-eval-harness, Inspect AI, Light Eval) — you know the strengths and limits of benchmarks like MMLU and GSM8K and when they don't apply, and you treat eval sets as versioned, frozen, regression-tracked code.
• Experience evaluating both generation and representation/classification: model-as-a-judge for generative quality and precision/recall, PR-AUC, retrieval/MTEB-style metrics, gold-label denoising, and label-noise handling.
• Deep understanding of Continuous Pre-training (CPT), Instruction Tuning (SFT), and how data quality shapes model behavior.
• Fluency in Python; strong data-pipeline and eval-harness engineering (e. g., Hugging Face Transformers, v LLM, lm-eval-harness).

Working knowledge of Py Torch and distributed training (FSDP2, Deep Speed Ze RO-3) sufficient to direct and debug post-training runs.

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